Beyond Traditional Covariates in Medical Informatics

Uri Kartoun

doi:10.20944/preprints201911.0073.v1

Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 5 November 2019 / Approved: 7 November 2019 / Online: 7 November 2019 (09:25:04 CET)

Deep behavioral covariates (DBCs) introduced in this perspective form a new class of covariates that have the potential to enhance the performance of predictive models and improve analytics in clinical decision support applications. DBCs can measure how engaged a patient tends to be and how he or she tends to respond to events, and they may be highly predictive of the patient’s outcomes for a planned treatment. DBCs may potentially serve as a standard to measure patient engagement and activation and may form highly efficient mechanisms for improving patient outcomes.

deep behavioral covariates; clinical informatics; predictive modeling; electronic medical records; machine-learning; data-mining

Computer Science and Mathematics, Probability and Statistics

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Beyond Traditional Covariates in Medical Informatics

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